FoCAS related Dagstuhl Seminars

Modern systems are often structured as complex, multi-layered networks of interconnected parts, where different layers interact and influence each other in intricate and sometimes unforeseen ways. It is infeasible for human operators to constantly monitor these interactions and to adjust the system to cope with unexpected circumstances; instead systems have to adapt autonomously to dynamically changing situations while still respecting their design constraints and requirements. Because of the distributed and decentralized nature of modern systems, this usually has to be achieved by collective adaptation of the nodes comprising the system. In open systems exhibiting collective adaptation, unforeseen events and properties can arise, e.g. as side effects of the interaction of the components or the environment. Modelling and engineering collective adaptive systems (CAS) has to take into account such “emergent” properties in addition to satisfying functional and quantitative requirements.

Self-aware computing systems are best understood as a sub-class of autonomic computing systems. The term autonomic computing was first introduced by IBM in 2001 motivated by the concern that the ever-growing size and complexity of IT systems would soon become too difficult for human administrators to manage. IBM proposed a biologically-inspired approach to tackle this challenge by designing computing systems that manage themselves in accordance with high-level objectives from humans. Over the past decade, there has been much research activity in the field of autonomic computing and at least 8000 research papers have been written on the topic. The evolution of autonomic computing reflects a growing acceptance of the idea that, for an autonomic computing element or system to manage itself competently, it needs to exploit (and often learn) models of how actions it might take would affect its own state and the state of the part of the world with which it interacts.

Non-zero-sum games with quantitative objectives constitute a field of growing attraction that is of central interest in all scenarios of computer science where multi-agent reactive systems are studied. The purpose of the seminar is to push forward and integrate the rapidly developing research on non-zero-sum games in its many facets (e.g., timed games, priced timed games, stochastic games, energy games, games with multidimensional optimization criteria, etc.), and to build bridges to related fields, in particular to control engineering.

Quantitative models and quantitative analysis in Computer Science is receiving increased attention in order to meet the challenges from application areas such as Cyber Physical Systems. What is aimed at is a revision of the foundation of Computer Science where Boolean models and analyses are replaced by quantitative models and analyses in order that more detailed and practically useful answers can be provided. Recently, a large number of new models, toolsets, and new application domains have emerged. The theory of weighted automata has also developed, introducing extensions of the models which are motivated by the quantitative analysis of systems.